Computer implemented method and device for ranking items of data

ABSTRACT

A computer implemented method of ranking items of data stored in a database comprising a plurality of records, wherein each record is associated with one or more items of data. The method includes generating a concordance of the items of data associated with the records in the database. Each record is assigned to a first group of records or to a second group of records. For each item of data a first indicator is determined representative of its occurrences in the records of the first group. For each item of data a second indicator is determined representative of its occurrences in the records of the second group. For each item of data a score is determined representative of a discriminative power of that item of data on the basis of the first and second indicator of that item of data.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.14/293,387 filed Jun. 2, 2014, the content of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention concerns a data analysis system. More inparticular the invention relates to a computer implemented method and asystem for ranking items of data included in records in a database.

BACKGROUND TO THE INVENTION

These days much data is generated and stored in digital form. Since the1980s the world's capacity to digitally store information has increasedby over twenty percent per year. In 2012 every day 2.5 exabytes(2.5×10¹⁸) of data were created. Some parts of this data is publiclyavailable, other parts are in company data.

The term ‘big data’ is often used in this connection for a collection ofdata so large and complex that it becomes difficult to process using onhand database management tools or traditional data processingapplications.

Much of this data is stored in large databases, sometimes referred to asdata warehouses. Such databases can store millions or even billions ofrecords. Each record can be associated with thousands of items of data.

There is a general need to be able to query databases to uncover recordsthat correspond to a predetermined content, e.g. to determine whichrecords contain certain items of data. However, with the explosivegrowth of the number of data records it becomes increasingly difficultto determine queries that properly yield records that provide thedesired information. It will be clear that a query yielding a largenumber of records still leaves the user in doubt as to which record aremore or less relevant.

Therefore, there is a specific need to efficiently and intuitively querydatabases. It is also of great importance to be able to perform queriesin real time, i.e. with minimal delay. Delay times are often seen as asevere hindrance in querying, and may dissuade people from continuedquerying of a database. In other words, people simply give up and stopquerying if delay times are perceived as annoying. In present times ofrelatively fast computing, delay times of as little as a few tenths of asecond can already be perceived as prohibitively annoying.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the present invention there isprovided a computer implemented method of accessing a data setcomprising a plurality of records, wherein each record is associatedwith one or more items of data. The method comprises using the computerto receive a data query on the data set and assigning each record to anin-group or to an out-group with respect to the query. Thus the data setcan be divided into an in-group and an out-group. The computerdetermines words appearing in records of the in-group and generates auser interface representative of said words. The computer alsodetermines words appearing in records of the out-group and generates auser interface representative of said words. Words can be nouns, verbs,adjectives, etc. as encountered in text documents. Words can also beidentifiers, names, metadata, dates, flags, tags, derived data,numerical values or bandings etc. More in general, words are anythingwhich can be represented as text, including items of data extracted fromthe records which are actual words, text labels assigned to orcalculated from data included in the records—e.g. high-income or 30,000or some_kind_of_data_label.

Providing a selection of words in the in-group provides the advantagethat the user is enabled to select a different query item, related tothe previous query item. The user needs no personal knowledge toidentify such related query item, as the system extracts the potentialquery items from the records. Also, the need to type a new query itemmay be obviated as the user only needs to select a further query item.Providing a selection of words in the out-group provides the advantagethat the user is immediately made aware of what else is contained in therecords beside his initial query. The user can instantly see what isbackground or opposite to his previous query. This greatly enhancesefficiency of a query session. This also increases the chances offinding hard to uncover details in the records that otherwise wouldeasily be missed.

In accordance with a further aspect, the first and second group areoutput by the system in a first view, while in further views the systemoutputs data representative of the records in different formats. Thefurther views can e.g. provide geographical information relating to therecords, temporal information relating to the records, or relationalinformation relating to the records. This provides increasedunderstanding of the nature and content of the records.

In accordance with a further aspect, the views are coupled, so as toallow user selection of an item of data as input for a query in anyview. The remaining views are updated to reflect the selected item ofdata. As such, the invention fuses analytics and search.

In accordance with a further aspect, upon user selection of an item ofdata for a query, all views are instantaneously updated to reflect theselected item of data. This provides smooth user experience and enhancesa flow of querying.

In accordance with a further aspect, the items of data relate to text,e.g. words, images, moving images, and/or audio. E.g. for images animage cloud could be represented or alternatively the word cloud couldbe a list of tags of metadata for the images (or moving images).Similarly, for audio a cloud or list of tags or metadata for the audiocould be presented.

In accordance with a further aspect, a computer implemented method ofranking items of data stored in a database comprising a plurality ofrecords, wherein each record is associated with one or more items ofdata is provided. The method comprises using the computer to generate aconcordance of the items of data associated with the records in thedatabase. Each record is assigned to a first group of records or to asecond group of records, e.g. an in-group and an out-group. For eachitem of data a first indicator is determined representative of itsoccurrences in the records of the first group. For each item of data asecond indicator is determined representative of its occurrences in therecords of the second group. For each item of data a score is determinedrepresentative of a discriminative power of that item of data on thebasis of the first and second indicator of that item of data. Using thescore, it can easily be determined which items of data have the highestdiscriminative powers for the first group of records and which items ofdata have the highest discriminative powers for the first group ofrecords. A high discriminative power for records of the first groupindicates items of data having a high likelihood of occurring in arecord of the first group and a low likelihood of occurring in a recordof the second group. The higher the difference in the likelihoods, thehigher the discriminative power. The difference can be related to anabsolute difference of numbers of occurrences of the item of data in thefirst group and in the second group. This takes into account that itemsof data that hardly occur in the second group and only a few times inthe first group may have a high relative likelihood of occurring in thefirst group, but are less efficient for positively identifying recordsas being comprised in the first group. Similarly, a high discriminativepower for records of the second group indicates items of data having ahigh likelihood of occurring in a record of the second group and a lowlikelihood of occurring in a record of the first group. The higher the(absolute) difference in the likelihoods, the higher the discriminativepower.

These items of data can be used in generating data representing a userinterface representative of the first plurality of items of data and/orthe second plurality of items of data. The first plurality of items ofdata can e.g. be a word cloud of high relevance to in-group records. Thesecond plurality of items of data can e.g. be a word cloud of highrelevance to out-group records.

According to an aspect, a computer implemented method of ranking itemsof data stored in a database comprising a plurality of records, whereineach record is associated with one or more items of data is provided.The method comprises using the computer to assign an identifier to eachunique item of data, and generating a concordance of the items of dataand/or associated identifiers associated with the records in thedatabase. A list of representations is generated, each representationrepresenting a record of the plurality of records, and eachrepresentation including the identifiers of the items of data identifiedin the respective record. Each representation is assigned to a firstgroup of representations or to a second group of representations. Foreach identifier a first indicator is determined representative of itsoccurrences in the representations of the first group. For eachidentifier a second indicator is determined representative of itsoccurrences in the representations of the second group. For eachidentifier a score is determined representative of a discriminativepower of that identifier on the basis of the first and second indicatorof that identifier. By using the representations and/or identifierscomputation on the actual records and items of data can be minimized.The list of representations can be a table, of e.g. integer values, within rows the individual records and in columns the unique items of datain the concordance (or vice versa). This allows for minimizing overallcomputation times, and allows for real-time querying and instantaneousupdate of views even when using very large databases including hundredsof millions of records or more.

According to a further aspect, a data processing system for rankingitems of data is provided.

According to a further aspect, a computer readable medium storingcomputer implementable instructions which when implemented by aprogrammable computer cause the computer to perform the method accordingto the invention is provided.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described in detailwith reference to the accompanying drawings in which:

FIG. 1 is a schematic block diagram of a system in accordance with anembodiment of the present invention;

FIG. 2 is a schematic flow chart of a method in accordance with anembodiment of the invention;

FIG. 3 is a schematic block diagram of a data processing system inaccordance with an embodiment of the invention;

FIGS. 4A-4E are schematic representations of a simplified example ofdetermining a score;

FIG. 5 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 6 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 7 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 8 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 9 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 10 is a schematic representation of a user interface in accordancewith an embodiment of the invention;

FIG. 11 is a schematic representation of a user interface in accordancewith an embodiment of the invention; and

FIG. 12 is a schematic representation of a user interface in accordancewith an embodiment of the invention.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 shows a schematic block diagram of a system 1 in accordance withan embodiment of the present invention. The system 1 includes a database2. The database 2 includes a plurality of records 4. The records can forinstance include texts, images, video fragments, audio fragments etc.Each record 4 is associated with one or more items of data. The items ofdata can e.g. be text items, such as words or phrases, included in therecord 4. Words can also be identifiers, names, metadata, dates, flags,tags, derived data, numerical values or bandings, timestamps etc. Theitems of data can also be images, such as moving images, or fragmentsthereof. The items of data can also be geographical data, temporal data,connectivity data, etc.

The system 1 further includes a data processing system 6 incommunication with the database 2. The system 1 further includes adisplay 8 in communication with the data processing system 6. The dataprocessing system 6 is arranged for generating data representing a userinterface. The user interface is displayed on the display 8. In FIG. 1the user interface includes a first view 10 including a word cloud ofitems of data of records 4 of the database 2. In this particular examplethe records relate to email messages and the word cloud includes itemsof data in the form of words appearing in the emails as described inU.S. patent application Ser. No. 13/102,648 published as US2012/284155(now U.S. Pat. No. 8,768,804, issued on Jul. 1, 2014) incorporatedherein by reference. The senders and recipients of the email messages inthe database are represented by positions around the edge of the circleand the existence of an email message is shown by the presence of a lineconnecting the points associated with a sender and the recipient(s). InFIG. 1 the user interface includes a second view 12 including a circularrepresentation of items of data of records 4 of the database 2. In thisparticular example the circular representation includes items of data inthe form of sender-recipient relationships in the emails. The system 1further includes an input unit, such as a keyboard, mouse and/or touchunit 14 in communication with the data processing system 6.

As will be described below, the user interface, especially the wordcloud, allows for highly efficient browsing through the records of thedatabase 2. Also, the user interface provides a transparent andintuitive way of browsing. Further, as will be described below, the userinterface assists in refining a query of the database. Thereto, the dataprocessing system can propose items of data that have highdiscriminative power favoring in-group records that comply with thepresent query. As will be highlighted below, the data processing systemcan also propose items of data that have high discriminative powerfavoring out-group records that do not comply with the present query.Items of data having a high discriminative power favoring in-grouprecords are items of data that have a high likelihood of occurring in anin group record and a low likelihood of occurring in an out grouprecord. Items of data having a high discriminative power favoringout-group records are items of data that have a high likelihood ofoccurring in an out group record and a low likelihood of occurring in anin group record.

In FIG. 1 the word cloud includes both words having high discriminativepower for in-group records and words having high discriminative powerfor out-group records. It has been found that the user interfaceincluding items of data having high discriminative power for in-grouprecords and items of data having high discriminative power for out-grouprecords increases the efficiency of browsing through the database. It,inter alia, provides insight into what has been selected by the presentquery versus what other information is contained in the database. It canalso help identify what information (e.g. which items of data) relate tobackground information rather than to foreground information that hasbeen selected by the user. Knowledge of background information also aidsin quickly focusing a query towards a desired result.

FIG. 3 shows an example of a schematic representation of a dataprocessing system 6 according to the invention. The data processingsystem 6 is associated with a database 2 storing a set of records. Theprocessing system 6 includes a retrieval unit 20 arranged for retrievingrecords from the database 2. As will be explained below, the dataprocessing system 6 further includes an identification unit 22 arrangedfor identifying in each record one or more items of data. A generationunit 24 is arranged for generating a concordance of the items of dataidentified in the records. The data processing system further includesan assignation unit 26 arranged for assigning each record to a firstgroup of records or to a second group of records. A conversion unit 30may be included for generating a list of representations, eachrepresentation representing a record in the database 2. The dataprocessing system further includes a processing unit 34 arranged fordetermining for each item of data a first indicator representative ofits occurrences in the records of the first group, determining for eachitem of data a second indicator representative of its occurrences in therecords of the second group; and determining for each item of data ascore representative of a discriminative power of that item of data onthe basis of the first and second indicator of that item of data. Thedata processing system 6 includes, or is associated with, a memory 28for storing the concordance and/or the list of representations. The dataprocessing unit further includes an input unit 32 for receiving a userinput and an output unit 36 for outputting information towards the user.

An embodiment of the invention will now be explained in more detail inrelation to FIG. 2 and FIG. 3. In this embodiment, the method starts bypreprocessing 100 the records 4 contained in the database 2. Thereto, aretrieval unit 20 of the data processing system 6 retrieves 102 allrecords from the database. In the example mentioned in relation to FIG.1, the retrieval unit 20 retrieves all email messages from the database2. FIG. 4A shows a simplified example for four records, each containinga text of a few words. An identification unit 22 identifies 104 items ofdata included within the records 4. In the example of FIG. 4A theidentification unit 22 identifies all unique words within the text dataof the records. In this example, the identification unit 22 furtherassigns 108 a unique identifier to each unique identified item of data.A generation unit 24 then generates a concordance of all unique items ofdata. The concordance for the simplified example of FIG. 4A is shown inFIG. 4B. The concordance can include the unique identifiers. In thisexample, the preprocessing 100 also includes generating 114, by ageneration unit 24, a list of representations. Each representationrepresents a record of the database and includes the unique items ofdata, and/or the corresponding unique identifiers, occurring in thatrecord. FIG. 4C shows the representations of the records of thesimplified example of FIG. 4A. In an embodiment, the representation mayalso include data representative of a prevalence of each occurring itemof data within the record.

It will be appreciated that in practice the concordance can be modifiedfor optimizing the concordance for the purpose of browsing the records4. The concordance may be optimized such that the included items of datarepresent relevant query items.

Thereto, in step 112, certain items of data may be removed from theconcordance. It will be appreciated that for example stop words can beomitted from the concordance. Stop words are words which do not containimportant significance to be used in search queries. Common stop wordsthat can be eliminated are “a”, “the”, “is”, “was”, “on”, “which”, etc.It will be appreciated that such stop words are generally known to theperson skilled in the art and lists of stop words are readily available.It will also be appreciated that a list of applicable stop words may bedependent on the content of the database.

Also, in step 112 certain items of data can be combined. It will beappreciated that words may be combined, e.g. by stemming or conversionto lower case. Stemming is a process for reducing inflected (orsometimes derived) words to their stem, base or root form. Stemmingalgorithms are known per se and readily available in the art.Alternatively, or additionally, combining of items of data may beperformed by the user, e.g. in a teach mode. Thereto a functionality canbe provided in which the user can indicate that certain items of dataare to be combined. The functionality can then e.g. assign the sameunique identifier to those items of data.

Also, in step 112 certain items of data may be split. It will beappreciated that words may be split, e.g. by disambiguation. Word-sensedisambiguation (WSD) is a process of identifying which sense of a word(i.e. meaning) is used in a sentence, when the word has multiplemeanings. For instance the word “bank” can refer to an establishment formonetary transactions as well as to a rising ground bordering a river,depending on the context. The concordance may include a unique entry foreach meaning of a word. It will be appreciated that when determining towhich meaning an occurrence of such word in a record relates, thecontext of said word (e.g. words in close proximity to said word) can betaken into account. Splitting of items of data may be performed by theuser, e.g. in a teach mode. Thereto a functionality can be provided inwhich the user can indicate that certain items of data are to be split.

The removing, combining and/or splitting may be executed uponidentification of the items of data, upon assigning the uniqueidentifiers, and/or upon generating the concordance. The concordance canbe stored in a memory 28 associated with the data processing unit 4, sothat the concordance need not be updated or determined again unless thecontent of the database changes.

Further, in preprocessing 100 a conversion unit 30 of the dataprocessing system 6 converts the records to a list of representations.For each record an associated representation is generated 114. It willbe appreciated that the conversion unit 30 may remove duplicates ofrecords. Each representation is a list of items of data, or theassociated unique identifiers, that occur in the respective record. Ifdesired the representations may include information on a prevalence ofthe respective items of data in the respective record. FIG. 4C shows anexample of a list of representations for the records of the simplifiedexample of FIG. 4A. The representations can be stored in the memory 28so that the representations need not be updated or determined againunless the content of the database changes. It will be appreciated thatthe representations form a much smaller amount of data to be stored thanthe associated records. The list of representations can be a table, ofe.g. integer values, with in rows the individual records and in columnsthe unique items of data in the concordance (or vice versa).

Thus the preprocessing 100 of the records yields the concordance and thelist of representations. The result of preprocessing can be used forgenerating 116 data representing a user interface representative of theconcordance. The data processing system 6 can determine a frequency ofoccurrence in the combined records of the items of data included in theconcordance. Such frequency of occurrence can relate to the total numberof occurrences of each item of data. Such frequency of occurrence canalso relate to the number of records in which each item of data occursat least once as in the example of FIG. 4E.

FIG. 5 shows a schematic representation of a generated 116 userinterface in relation to preprocessing 100. This example relates to adatabase 2 including a large number of records 4 in the form of emailmessages. The email messages contain text. The text includes content,but also sender names, recipient names, addresses, dates, times, flags(“private”, “confidential”, “request read receipt”, etc.). The text canalso be included in attachments with text content etc. The text relatingto the email message can also be metadata, for instance that that theemail message had been marked as junk email, the message has not beenread, the message has been recalled, or the like. The records 4 includeitems of data in the form of words of the texts. In the situationdepicted in FIG. 5 preprocessing 100 has been performed. In this examplethe forty most frequently occurring words are displayed in view A in theform of a word cloud 40. It will be appreciated that stop words havebeen eliminated in the example of FIG. 5.

In a second view B the user interface displays data representative ofthe records in a different format. In FIG. 5 view B presents datarepresentative of all records in the database. View B presents datarepresenting the combination of sender and recipient(s) of each email inthe database represented as a line in the circular graph. Thecircumference of the circular graph in view B represents items of datarelating to email users (senders and receivers) of the email messages inthe database. Interactions between the email users are represented aslines connecting a sender with one or more receivers of the associatedemail message, as described in WO2012/152726 and US2014/0132623, bothincorporated herein by reference.

Next, a user query 200 may be performed on the database. Thereto a userselects an item of data by means of an input unit 28. The input unit maybe a keyboard, mouse, touchpad, touch functionality of a touch screen,microphone, camera or the like. The item of data may be selected 204from the first view A or may be selected 202 from the second view B.FIG. 5 shows an example of performing a query by selecting 202 an itemof data from view B. In the example the selection concerns the emailssent to or from a particular person, indicated in black at 44.

In response to receipt of the user selection, the data processing system6 processes 206 the user selection. Thereto, the data processing systemdetermines the item of data or items of data associated with the userselection. In this example, the data processing system 6 determines theword, here the name, associated with the sender of the selected streamof email messages. This selection of items of data forms the user queryto be performed on the records 4 in the database 2.

For performing the user query, the data processing system 6 startsprocessing step 300. An assignation unit 26 assigns 302 each record 4 toa first group of records or to a second group of records. Here the firstgroup constitutes an in-group, i.e. a group of records that complieswith the user query. Here the in-group contains the records thatcomprise the selected items(s) of data, i.e. the name of the sender. Itwill be appreciated that it is not necessary that all records indicatethe selected item of data as the sender of that particular emailmessage. Also records containing the selected item of data as recipient,or as part of the content of the email message, will form part of thein-group. Here the second group constitutes an out-group, i.e. a groupof records that does not comply with the user query. Here the out-groupcontains the records that do not comprise the selected items(s) of data.FIG. 4D shows how the records of the simplified example of FIG. 5A areassigned to an in-group and an out-group in response to a fictionalquery relating to the word “this”.

Next, a processing unit 34 of the data processing system 6 determines304, 306 for each item of data a first indicator and a second indicator.The first indicator is representative of the occurrences of therespective item of data in the records of the first group. In anembodiment the processing unit takes the representations of the recordsin the first group and for each item of data sums the occurrences ofthat item of data, or the unique identifier thereof, in therepresentations of the records in the first group. This sum then can bethe first indicator. If the representations include a prevalence, thisprevalence can be taken into account when determining the firstindicator. The second indicator is representative of the occurrences ofthe respective item of data in the records of the second group. In anembodiment the processing unit takes the representations of the recordsin the second group and for each item of data sums the occurrences ofthat item of data, or the unique identifier thereof, in therepresentations of the records in the second group. This sum then can bethe second indicator. If the representations include a prevalence, thisprevalence can be taken into account when determining the secondindicator. FIG. 4E shows the determination of the first indicator I₁ andthe second indicator I₂ for each item of data by summing the occurrences(“0” or “1”) of that item of data for records 2 and 3 (firstgroup/in-group) and for records 1 and 2 (second group/out-group) in thelist of representations respectively. As the processing unit can takethe representations of the records and for each item of data sums theoccurrences of that item of data, or the unique identifier thereof, inthe first and second group of records, the processing for determiningthe first and second indicator can be (NR-2) simple additions of e.g.integer values, with NR being the number of records in the database. Forthe entire database only NI sets of first and second indicators need tobe determined, with NI being the number of items of data in theconcordance. Therefore, the amount of processing for the entire databaseis extremely limited, the bulk of heavy calculation being done inpreprocessing. This makes the process highly suitable for handling bigdata. With the first indicator and the second indicator, the processingunit 34 can determine 308 for each item of data a score S representativeof a discriminative power of that item of data. The score S can berepresentative of the discriminative power of the item of data for thefirst or second group of records. A high discriminative power forrecords of the first group indicates an item of data having a highlikelihood of occurring in a record of the first group and a lowlikelihood of occurring in a record of the second group. A highdiscriminative power for records of the second group indicates an itemof data having a high likelihood of occurring in a record of the secondgroup and a low likelihood of occurring in a record of the first group.The score S can in addition also be representative of a prevalence ofthe item of data in the first group or in the second group. It will beappreciated that an item of data that occurs very few times in therecords, may have a high likelihood of occurring more often in one ofthe two groups, but due to its low prevalence still can have a lowdiscriminative power with respect to that group as a whole. Therefore,in an embodiment the score S takes prevalence into account as well. Inan embodiment the highest scores are associated with items of data thathave the highest discriminative power for records of the first group andthe lowest (or largest negative) scores are associated with items ofdata that have the highest discriminative power for records of thesecond group. In the example of FIG. 4E the scores are calculated usingthe formula S=(I₁ ^(1.5)−I₂ ^(1.5))/(I₁+I₂). This formula yields anincreased positive or negative score for items of data having both ahigher likelihood of occurring in one of the two groups and having ahigher prevalence. More in general, other formulae can be used as well.The score S can e.g. be calculated as S=(I₁ ^(N)−I₂ ^(N))/(I₁+I₂)^(M),wherein I₁ is the first score, I₂ is the second score, N is a parameterbetween ⅓ and 3 and M is a parameter between ⅓ and 3. Optionally, N isbetween 1 and 2. Optionally M is between 0.5 and 1. The score can alsobe calculated as S=(I₁ ^(N)−I₂ ^(N))/(I₁ ^(M)+I₂ ^(M)),S=(I₁−I₂)^(N)/(I₁+I₂)^(M), or S=(I₁−I₂)^(N)/(I₁+I₂)^(M). The bestformula for calculating the score S can depend on the nature of the datastored in the database.

When the scores for all items of data have been determined, theprocessing unit 34 determines 310 a first plurality (e.g. apredetermined number) items of data having the highest discriminativepower for records of the first group and determines 312 a secondplurality (e.g. a predetermined number) of items of data having thehighest discriminative power for records of the second group. In thepresent example the first plurality of items of data includes the itemsof data having the highest scores. In the present example the secondplurality of items of data includes the items of data having the lowest(most negative) scores. The processing unit 34 may sort the items ofdata according to their scores for this.

Thus the processing 300 yields the first and second plurality of itemsof data. The result of processing can be used for generating datarepresenting a user interface representative of the first and secondplurality of items of data. This can be done in step 400 for updatingthe views A and B. In FIG. 6 the first view A shows the first plurality48 of items of data, here the top forty words (underlined), and thesecond plurality 50 of items of data, here the bottom forty words (notunderlined). The first and second plurality are visualized as a wordcloud 40. It will be appreciated that the selected item of data(selected at 44 in view B of FIG. 6) is also among the first pluralityof items of data as indicated at 46, viz. the word (name) “dasovich”. Itwill be appreciated that the word cloud 40 can be constructed to providean indication of the score. In this example a font size of the items ofdata (words) in the word clouds is scaled according to the absolutevalue of the score S associated with the respective item of data. It isalso possible the word cloud 40 can be constructed to provide anindication of an average distance between two items of data of one groupwithin the texts of the records of that group. In this example adistance in between two items of data (words) in the word clouds isscaled according to an average distance between said two items of datawithin the corresponding records.

FIG. 6 showed a user selection in the second view B resulting in a wordcloud 40 containing items of data from the in-group as well as items ofdata from the out-group. It is noted that due to the use of theconcordance and list of representations the inventors have succeeded inproviding real-time updating of the first view A in response to a userselection in the second view B.

It is also possible to select an item of data in the first view A. FIG.7 shows an example of a user interface when in the first view A of FIG.6 the item of data “california” is selected at 52. Similarly asdescribed above, the assignation unit 26 assigns 302 each record 4 to afirst group of records or to a second group of records. Here the firstgroup constitutes the in-group, i.e. the group of records including theword “california”. Here the second group constitutes the out-group, i.e.the group of records not including the word “california”. With therecords re-assigned to the first and second groups, the first indicatorI₁, the second indicator I₂, and the score S for each item of data canbe determined. It will be appreciated that the concordance and the listof representations need not be determined anew, saving valuableprocessing time. With the recalculated scores for each item of data, thefirst plurality of items of data and the second plurality of items ofdata can be determined anew. FIG. 7 shows in the first view A a wordcloud including these redetermined first and second pluralities of itemsof data. Simultaneously, the second view B is updated. The selected itemof data “california” is used to determine all email messages includingthe word “california”. The graphical representation of these emailmessages is shown in black at 56 in the second view B of FIG. 7 inaccordance with US2014/0132623, incorporated herein by reference.

FIG. 8 shows an example of a user interface when in the first view A ofFIG. 6 the item of data “senate” is selected at 54. Similar as explainedin relation to FIG. 7 the first view A is updated due to the selectionof the item of data “senate”. Similarly, the second view B is updated inaccordance with US2014/0132623. The update indicates the recordsincluding “senate” in black at 58. The example of FIG. 8 includes athird view C. In this third view C the user interface displays datarepresentative of the records in yet a different format. In FIG. 8 viewC presents data representative of a distribution of email messages as afunction of time. In horizontal direction the sender-recipientinteractions of the records are shown. Horizontal lines representconnections from a sender to a recipient for the records in thedatabase. The senders and recipients are indicated at the top of thegraph. In the vertical direction it is indicated at which moment in timethe email message was sent. View C is updated in view of the selecteditem of data “senate” as described in US2014/0059456, incorporatedherein by reference. The update indicates the records including “senate”in black at 60.

It will be appreciated that in the example of FIGS. 6-8 the multipleviews, and the possibility to select items of data for querying thedatabase provides highly useful possibilities for interactively queryingthe database. It is for example possible to select a word, such as“california” as shown above and instantaneously see the email paths(sender-recipient) that have a high occurrence of said word, andsimultaneously and instantaneously see the temporal changes in theoccurrence of the word in the records. From this the user can continueby selecting the email paths just indicated as relevant in view of“california” occurring in the records, and see in the first view wordsrelated to these email paths. This may initiate a query based on anotherword than “california”. Alternatively, the user could continue byselecting a time slot indicated as relevant in view of “california”occurring in the records, and see in the first view words related tothis time slot. This may initiate a query based on yet another word than“california”. Also, the first view provides insight in other words thathave a high discriminative power for records including the word“california”, which can be selected for further querying. Further, thefirst view provides insight in other words that have a highdiscriminative power for records not including the word “california”.These too may be used as user selection for further querying. As such,the invention fuses analytics and search. It has been found that inqueries that are aimed at uncovering hard-to-find information theout-group information can be particularly useful in arriving at queryitems that lead to the desired results. Moreover, as will be appreciatedfrom the above, the entire querying can be performed without typing asingle word. This is very useful in preventing writer's block fromkeeping a user from querying the database.

FIGS. 9-12 relate to a further example. FIG. 9 shows a schematicrepresentation of a generated 116 user interface in relation topreprocessing 100. This example relates to a database 2 including alarge number of records 4 in the form of police reports. The policereports contain text. The text includes content, but also police officeridentification, names, addresses, dates, times, etc. The records 4include items of data in the form of words of the texts. In thesituation depicted in FIG. 9 preprocessing 100 has been performed. Thus,the concordance and the list of representations is determined asdescribed above. In this example the twenty most frequently occurringwords are displayed in view A in the form of a list 62 of words. In thisexample the list 62 is an ordered list. The most frequently occurringitem of data is here positioned at the top of the list, the next mostfrequently occurring item of data at the next position, and so on. Itwill be appreciated that stop words have been eliminated in the exampleof FIG. 9.

In a second view B the user interface displays data 64 representative ofthe records in a different format. In FIG. 9 view B presents data 64representative of a distribution of police reports as a function oftime. It will be appreciated that the records thereto include items ofdata relating to time. In vertical direction a numerical index of therecords is shown. In this example the numerical index is representativeof a police route corresponding to the report. In the horizontaldirection it is indicated at which moment in time the police report wasfiled.

In a third view C the user interface displays data 66 representative ofthe records in yet a different format. In FIG. 9 view C presents data 66representative of all records in the database. In this example therecords include data representative of a geographical location. View Cpresents data representing for each record in the database thegeographical location associated with that record represented as a doton a representation of a map as described in U.S. patent applicationSer. No. 14/215,238, incorporated herein by reference.

Next, a user query 200 may be performed on the database. Thereto a userselects an item of data by means of an input unit 28. The item of datamay be selected 204 from the first view A, the second view B or thethird view C. FIG. 10 shows an example of performing a query byselecting an item of data from view C. In the example the selectionconcerns a geographical area indicated at 68. The geographical area isselected by selecting an area in the representation of the map. The areacan e.g. be selected by drawing a contour, such as a rectangle, e.g. byusing the mouse.

In response to receipt of the user selection, the data processing system6 processes 206 the user selection. Thereto, the data processing systemdetermines the items of data associated with the user selection. In thisexample, the data processing system 6 determines the geographicalindicators associated with the police reports having a geographicalindicator that falls within the selected area. This selection of itemsof data forms the user query to be performed on the records 4 in thedatabase 2.

For performing the user query, the data processing system 6 startsprocessing step 300. The assignation unit 26 assigns 302 each record 4of the database 2 to a first group of records or to a second group ofrecords. Here the first group constitutes an in-group, i.e. the recordsthat comprise the selected items(s) of data, i.e. the geographicalindicator corresponding to the selected area. Here the second groupconstitutes an out-group, i.e. the records that do not comprise theselected items(s) of data, i.e. the geographical indicator correspondingto the selected area.

With the records assigned to the first and second groups, the firstindicator I₁, the second indicator I₂, and the score S for each item ofdata can be determined as described above. It will be appreciated thatthe concordance and the list of representations need not be determinedanew, saving valuable processing time. FIG. 10 shows in the first view Aa first list 70 of items of data representative of the first pluralityof items of data. FIG. 10 shows in the first view A a second list 72 ofitems of data representative of the second plurality of items of data.The first and second lists are ordered lists in this example.

Simultaneously, the second view B is updated. The selected items of datadetermine all records associated with the police reports having ageographical indicator that falls within the selected area. Thegraphical representation of these police reports as black dots at 74 inthe second view B of FIG. 10. In this example the numerical indexes ofthe records associated with the selected geographical area are mainly inthe range of 1100-1150 and 1500-1550. These numerical indexes correspondto police routes within the selected geographical area.

It is also possible to select an item of data in the first view A. FIG.11 shows an example of a user interface when in the first view A of FIG.9 or FIG. 10 the item of data “heroin” is selected at 76 from the firstlist 70. Similarly as described above, the assignation unit 26 assigns302 each record 4 to a first group of records or to a second group ofrecords. Here the first group constitutes the in-group, i.e. the groupof records including the word “heroin”. Here the second groupconstitutes the out-group, i.e. the group of records not including theword “heroin”. With the records re-assigned to the first and secondgroups, the first indicator I₁, the second indicator I₂, and the score Sfor each item of data can be determined. It will be appreciated that theconcordance and the list of representations need not be determined anew,saving valuable processing time. With the recalculated scores for eachitem of data, the first plurality of items of data and the secondplurality of items of data can be determined anew. FIG. 11 shows in thefirst view A the first list 70 of words according to the redeterminedfirst plurality of items of data. FIG. 11 shows in the first view A thesecond list 72 of words according to the redetermined second pluralityof items of data. In this example the first list 70 contains fewer itemsof data than the second list.

Simultaneously, the second view B is updated. The selected item of data“heroin” is used to determine all records including the word “heroin”.The records associated with the police reports including the word“heroin” are indicated as black dots at 78 in the second view B of FIG.11. It will be appreciated that in this example the records includingthe item of data “heroin” are spread out over many numerical indexes andspread out in time. However, it is for instance possible to see temporaleffects in the occurrence of the word “heroin” in the records. At 79 forexample a temporal increase of the occurrence of the word “heroin” inthe records can be observed.

Simultaneously, the third view C is updated. The selected item of data“heroin” is used to determine all records including the word “heroin”.The records associated with the police reports including the word“heroin” are indicated as white dots at 80 in the third view C of FIG.11. It will be appreciated that in this example the records includingthe item of data “heroin” are spread out over a large geographicalrange.

It is also possible to select an item of data in the second view B. FIG.12 shows an example of a user interface when in the second view B ofFIG. 9, FIG. 10, or FIG. 11 a range 82 of numerical indexes in the rangeof 100-150 in a certain time period is selected. In response to receiptof the user selection, the data processing system 6 processes 206 theuser selection. Thereto, the data processing system determines the itemsof data associated with the user selection. In this example, the dataprocessing system 6 determines the numerical indexes and time stampsassociated with the police reports within the selection. This selectionof items of data forms the user query to be performed on the records 4in the database 2.

For performing the user query, the data processing system 6 startsprocessing step 300. The assignation unit 26 assigns 302 each record 4of the database 2 to a first group of records or to a second group ofrecords. Here the first group constitutes an in-group, i.e. the recordsthat comprise a numerical index and time stamp associated with thepolice reports within the selection. Here the second group constitutesan out-group, i.e. the records that do not comprise the selecteditems(s) of data, i.e. do not comprise both a numerical index and timestamp associated with the police reports within the selection.

With the records assigned to the first and second groups, the firstindicator I₁, the second indicator I₂, and the score S for each item ofdata can be determined as described above. It will be appreciated thatthe concordance and the list of representations need not be determinedanew, saving valuable processing time. FIG. 12 shows in the first view Aa first list 70 of items of data representative of the first pluralityof items of data. FIG. 12 shows in the first view A a second list 72 ofitems of data representative of the second plurality of items of data.The first and second lists are ordered lists in this example.

Simultaneously, the third view C is updated. The selected items of data,i.e. the numerical indexes and time stamps within the selection are usedto determine all records including a numerical index and time stampwithin the selection. These records are indicated as white dots at 84 inthe third view C of FIG. 12. It will be appreciated that in this examplethe records including a numerical index and time stamp within theselection are concentrated in downtown Chicago.

It will be appreciated that in the example of FIGS. 9-12 the multipleviews, and the possibility to select items of data for querying thedatabase provides highly useful possibilities for interactively queryingthe database. It is for example possible to select a word, such as“heroin” as shown above and immediately see the geographical areas thathave a high occurrence of said word, and simultaneously see the temporalchanges in the occurrence of the word in the records. From this the usercan continue by selecting the geographical area just indicated asrelevant in view of “heroin” occurring in the records, and see in thefirst view words related to this geographical area. This may initiate aquery based on another word than “heroin”. Alternatively, the user couldcontinue by selecting a time slot indicated as relevant in view of“heroin” occurring in the records, and see in the first view wordsrelated to this time slot. This may initiate a query based on yetanother word than “heroin”. Also, the first view provides insight inother words that have a high discriminative power for records includingthe word “heroin”, which can be selected for further querying. Further,the first view provides insight in other words that have a highdiscriminative power for records not including the word “heroin”. Thesetoo may be used as user selection for further querying.

Herein, the invention is described with reference to specific examplesof embodiments of the invention. It will, however, be evident thatvarious modifications and changes may be made therein, without departingfrom the essence of the invention. For the purpose of clarity and aconcise description features are described herein as part of the same orseparate embodiments, however, alternative embodiments havingcombinations of all or some of the features described in these separateembodiments are also envisaged.

In the above example, the records included text. It will be appreciatedthat the records can also include images, such as moving images, and/oraudio data. In case the records include images, the first list of itemsof data and the second list of items of data can include images, such asmoving images. The images can be selected as in-group or out-group, e.g.on the basis of a description of the image in text, and/or on the basisof image attributes, such as color, subject (portrait, landscape, car,etc.), contrast, etc. Of course, these records can also contain timestamps, geospatial information, information on interrelation betweenrecords, etc. that can be used for querying the records, e.g. forselecting images as in-group or out-group. In case records relate toaudio data, the records can include transcripts, e.g. automaticallygenerated transcripts, of audio data.

In the above examples, stop words are removed from the concordance.Since stop words have a high chance of occurring in many records, stopwords are likely to have a very low discriminative power for records.Stop words have a large likelihood of equally occurring in records ofthe first group and records of the second group. Therefore, as will beappreciated, it is not always necessary to remove stop words. The needto remove stop words may depend on the nature of the data in thedatabase. It will be appreciated that in the above examples the removalof stop words is not necessary.

In the above examples, the combining of words was presented on the basisof linguistics. It will be appreciated that it is also possible tocombine items of data on the basis of the function of the items of data.For instance, it is possible that the concordance has one combined entry“name” for all instances of personal names, e.g. so as to anonymize theprocessing. It is also possible that the concordance has one or morecombined entries for scalar items of data. The concordance could forinstance include two entries “high temperature” and “low temperature”and assign “high temperature” to records including an item of datarepresentative of a temperature above a predetermined threshold, andassign “low temperature” to records including an item of datarepresentative of a temperature below said predetermined threshold. Alsofor this purpose a functionality can be provided in which the user canindicate that certain items of data, or certain types of items of data,are to be combined.

In the above example, the records are assigned to a first group ofrecords or to a second group of records. It will be appreciated that therecords can also be assigned to a first group of records, a second groupof records, or to one or more further groups of records. For example, inthe example of FIG. 10, the records comprising the selected geographicalindicator could be divided into an in-group, the records comprising ageographical indicator different from the selected geographicalindicator could be divided into an out-group, and the records notcomprising a geographical indicator could be divided into a rest-group.

It is also possible that the user query is performed as a multi-stepprocess. In a first step a preselection could be performed, e.g.selecting only those records that have a geographical indicator relatingto Chicago or New York. This preselection would yield a sub-set of thedata set included in the database. Next, on this sub-set in a secondstep a query is performed. This second step could e.g. compriseselecting the word “Chicago”. Then an in-group (within the sub-set)would include all records including the word “Chicago” and an out-group(within the sub-set) would include all records including the word “NewYork”. Hence, in the first step a sub-set of the database is selected,and in the second step a selection within the sub set is made. This canprovide increased possibilities for uncovering information ondifferences between the two groups (“Chicago” and “New York”) that areeffectively compared in the second step. It will be appreciated that itis also possible to add further preselection steps for limiting thesub-set of the database that the query is performed on.

In the above examples, the records relate to emails and police reports.However, the applicability of the present invention is much wider. In asimilar way as described above, the invention can be used for queryingother databases including records containing items of information. Byway of non-exhaustive list some more exemplary embodiments are givenbelow.

As a third example the records in the database relate to litigationdocuments. The litigation documents may include email messages, memos,instructions, specification sheets, letters, faxes, reports, etc. Theselitigation documents can contain text, time stamps, geospatialinformation, information on interrelation between records, etc. Thedatabase of litigation documents relating to a single court case cancontain hundreds of thousands records. In order to uncover informationfrom such vast amount of records of diverse nature, querying thedatabase according to the invention can be used. Hence the user canselect items of data for querying in words, time slots, geographicalareas, etc. The concept of using the in-group and out-group can also beused for efficiently unearthing both incriminating and discharginginformation. When the in-group relates to incriminating items of data,there is a high likelihood that the out-group relates to dischargingitems of data (and vice versa).

As a fourth example, the invention is put to use for sales forcemanagement. The records in the database can relate to contactrelationship management records, such as contact details, client contactreports, sales reports, commercial reports, payment behavior,quotations, distributor networks, commercial agreements, discountagreements, etc. These records can contain text, time stamps, geospatialinformation, information on interrelation between records, etc. Theinvention can be put to use to query the contact relationship managementrecords. Specific knowledge, commonly residing by sales managers, maynot be necessary to query the database. The concept of using thein-group and out-group can also be used for efficiently discriminatingbetween contacts certain agreements or rights and contacts not havingsuch agreements or rights.

As a fifth example, the records in the database relate to investmentdata. The investment data may include stock exchange information, sharevalues, accounting information, currency information, fund information,company information, market information, etc. These records can containtext, time stamps, geospatial information, information on interrelationbetween records, etc. The invention can be put to use to query theinvestment data. The intuitive querying can uncover relationshipsbetween data that is not obvious at first sight. The concept of usingthe in-group and out-group can also be used for discriminating betweenpositive and negative influences on investment decisions.

As a sixth example, the records in the database relate to social mediadata. The records can include entries on facebook, tweets, SMS-messages,etc. These records can contain text, time stamps, geospatialinformation, information on interrelation between records, etc. From thesocial media data for instance trends or hot topics can be determined.It can also be determined where trends occur, how trends spread or move.The concept of using the in-group and out-group can also be used foridentifying trends and counter-trends.

As a seventh example, the records in the database relate to technicalvisit reports. The technical visit reports data may include drawings,models, specification sheets, parts lists, version information, faultreports, photographs, etc. These records can contain text, time stamps,geospatial information, information on interrelation between records,etc. The invention can be put to use to query disparate documents.

As an eighth example, the records in the database relate to clinicaland/or pharmaceutical data. The clinical and/or pharmaceutical data mayinclude medical records, reports, clinical trial data, chemical formulafragments, chemical formulae, biological formulae fragments, biologicalformulae, mathematical formula fragments, mathematical formulae, etc.These records can contain text, time stamps, geospatial information,information on interrelation between records, etc. When the medicalrecords include patient identifiers, it is possible to combine patientidentifiers as a single entry in the concordance. This obscures thepatient identifiers in the query results, increasing privacy. Theconcept of using the in-group and out-group can e.g. be used foruncovering data related to effects of drugs in view of test groups andcontrol groups.

As a ninth example, the records in the database relate to forensicand/or law enforcement. The forensic and/or law enforcement data mayinclude drawings, models, photographs, reports, email messages,telephone calls, etc. These records can contain text, time stamps,geospatial information, information on interrelation between records,etc. The invention can be put to use to query disparate documents.

As a tenth example, the records in the database relate to technicaldocumentation data. The technical documentation data may includedrawings, models, specification sheets, instruction manuals, partslists, version information, reports, etc. These records can containtext, time stamps, geospatial information, information on interrelationbetween records, etc. The invention can be put to use to query disparatedocuments.

As an eleventh example, the records in the database relate to patentinformation. The records can include patent descriptions, bibliographicdata, applicant data, prior art citations, etc. These records cancontain text, time stamps, geospatial information, information oninterrelation between records, etc. The concept of using the in-groupand out-group can e.g. be used for uncovering data related totechnological fields in which companies are and are not active.

As a twelfth example, the records in the database relate to telephonecalls. The records can include voice recordings of telephone calls. Therecords can also include transcripts, e.g. automatically generatedtranscripts, of telephone calls. These records can contain text, timestamps, geospatial information, information on interrelation betweenrecords, etc.

As a thirteenth example, the records in the database relate to mixeddata. Such mixed data can e.g. be all data included on a company serveror company network, all data included on a personal hard disk or homenetwork, data included on a public network, such as (part of) theinternet. These records can contain text, time stamps, geospatialinformation, information on interrelation between records, etc. Theinvention can be put to use for querying such mixed data. The concept ofusing the in-group and out-group can e.g. be used for interactivebrowsing the data. The concept of using the in-group and out-group canalso be used for focusing a query when the desired outcome of the queryis not (completely) clear at the onset of a querying session.

As a fourteenth example, the records in the database relate to images,such as moving images. The database can e.g. include records relating toa plurality of movies, tv-series episodes or the like. It will beappreciated that such records can include still images, moving images,text descriptives etc. Of course, these records can also contain timestamps, geospatial information, information on interrelation betweenrecords, etc. that can be used for querying the records, e.g. forselecting movies as in-group or out-group with respect to userpreferences.

It will be appreciated that the retrieval unit, the identification unit,the generation unit, the assignation unit, the conversion unit, theoutput unit, the processing unit, and the input unit can be embodied asdedicated electronic circuits, possibly including software codeportions. The retrieval unit, the identification unit, the generationunit, the assignation unit, the conversion unit, the output unit, theprocessing unit, and the input unit can also be embodied as softwarecode portions executed on, and e.g. stored in, a memory of, aprogrammable apparatus such as a computer.

Although the embodiments of the invention described with reference tothe drawings comprise computer apparatus and processes performed incomputer apparatus, the invention also extends to computer programs,particularly computer programs on or in a carrier, adapted for puttingthe invention into practice. The program may be in the form of source orobject code or in any other form suitable for use in the implementationof the processes according to the invention. The carrier may be anyentity or device capable of carrying the program.

For example, the carrier may comprise a storage medium, such as a ROM,for example a CD ROM or a semiconductor ROM, or a magnetic recordingmedium, for example a floppy disc or hard disk. Further, the carrier maybe a transmissible carrier such as an electrical or optical signal whichmay be conveyed via electrical or optical cable or by radio or othermeans, e.g. via the internet or cloud.

When a program is embodied in a signal which may be conveyed directly bya cable or other device or means, the carrier may be constituted by suchcable or other device or means. Alternatively, the carrier may be anintegrated circuit in which the program is embedded, the integratedcircuit being adapted for performing, or for use in the performance of,the relevant processes.

However, other modifications, variations, and alternatives are alsopossible. The specifications, drawings and examples are, accordingly, tobe regarded in an illustrative sense rather than in a restrictive sense.

For the purpose of clarity and a concise description features aredescribed herein as part of the same or separate embodiments, however,it will be appreciated that the scope of the invention may includeembodiments having combinations of all or some of the featuresdescribed.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other features or steps than those listed in aclaim. Furthermore, the words ‘a’ and ‘an’ shall not be construed aslimited to ‘only one’, but instead are used to mean ‘at least one’, anddo not exclude a plurality. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to an advantage.

The invention claimed is:
 1. A computer implemented method of rankingitems of data stored in a database comprising a plurality of records,wherein each record is associated with at least one item of data,comprising: generating a concordance of the items of data associatedwith the records in the database, wherein the generating includespreprocessing the plurality of records for yielding the concordance andlist of representations using a conversion unit for converting therecords to the list of representations; assigning each record to one ofa first group of records and a second group of records; determining foreach item of data a first indicator representative of its occurrences inthe records of the first group; determining for each item of data asecond indicator representative of its occurrences in the records of thesecond group; and determining for each item of data a scorerepresentative of a discriminative power of that item of data on thebasis of the first and second indicator of that item of data, whereinthe score S is determined as S=(I₁ ^(N)−I₂ ^(N))/(I₁+I₂)^(M), wherein I₁is the first score, I₂ is the second score, N is a parameter between ⅓and 3, and M is a parameter between ⅓ and
 3. 2. The method of claim 1,wherein the first group consists of records that are within apredetermined query, and the second group consists of records data filesthat are outside said predetermined query, wherein the records includeat least one of texts, images, moving images, audio data, and whereinthe records include at least one of time stamps, geospatial information,and information on interralation between records.
 3. The method of claim2, wherein the predetermined query includes query items, wherein thequery items are at least one of words, groups of words, texts, imagefragments, images, video fragments, audio fragments, numbers, chemicalformula fragments, chemical formulae, biological formulae fragments,biological formulae, mathematical formula fragments, mathematicalformulae, statistical properties, and wherein the words include stopwords removed from the concordance due to a low discriminative power forrecords.
 4. The method of claim 1, wherein N is a parameter between 1and
 2. 5. The method of claim 1, wherein M is a parameter between 0.5and
 1. 6. The method of claim 1, wherein the concordance includes atleast one combined entry for scalar items of the data.
 7. The method ofclaim 1, wherein the database relates to one of litigation, sales forcemanagement, investment data, social media data, technical visit reports,clinical pharmaceutical data, forensic law enforcement data, technicaldocumentation data, and patent information.
 8. The method of claim 1,wherein the records in the database include mixed data, and wherein themixed data includes data on a company server, data on a company network,all data on a personal hard disk, all data on a home network and data ona public network.
 9. The method of claim 1, further comprising using thecomputer for determining a first plurality of items of data having thehighest discriminative powers for the first group of records.
 10. Themethod of claim 9, further comprising using the computer for determininga second plurality of items of data having the highest discriminativepowers for the second group of records.
 11. The method of claim 1,further comprising using the computer for determining a first pluralityof items of data having the highest discriminative powers for the firstgroup of records; and using the computer to generate data representing auser interface including a first view with data representative of thefirst plurality of items of data, and a second view with further datarepresentative of the records.
 12. A computer implemented method ofaccessing data stored in a database comprising a plurality of records,and querying the database in real-time, wherein each record isassociated with at least one items of data, comprising: performing adata query in real-time for retrieving records from the database;assigning each of the records to one of an in-group and an out-groupwith respect to the data query for dividing the data set into twogroups; determining and providing a selection of words appearing in therecords of the in-group and generating a user interface representativeof the words, wherein the words include nouns, verbs, adjectives, names,metadata, dates, flags, tags, derived data, numerical values, and anyother identifier represented as text; providing a selection of words inthe out-group for informing a user of information contained in therecords other than the query for comparison, wherein the user interfaceoutputs at least the in-group into a first view; and updatinginstantaneously all views for reflecting the selected item of data. 13.The method of claim 12, further comprising performing a multi-step userquery process including preselecting a group of records for producing afirst sub-set of the data, and performing a second query of the firstsub-set of the data for producing a second sub-set of the data.
 14. Themethod of claim 12, further comprising: determining for each item ofdata a first indicator representative of its occurrences in therepresentations of the in-group; determining for each item of data asecond indicator representative of its occurrences in therepresentations of the out-group; and determining for the each item ofthe data a score representative of a discriminative power of that saiditem of the data on the basis of the first indicator and the secondindicator of that said item of data, wherein the score S is determinedas S=(I₁ ^(N)−I₂ ^(N))/(I₁+I₂)^(M), wherein I₁ is the first score, I₂ isthe second score, N is a parameter between ⅓ and 3 and M is a parameterbetween ⅓ and
 3. 15. The method of claim 12, wherein the user interfaceis displayed on a display.
 16. The method of claim 12, wherein the firstview includes a word cloud of the items of the data of the records,wherein the records relate to email messages, and wherein the word cloudincludes items of data in form of words appearing in the email messages.17. The method of claim 12, wherein the user interface outputs theout-group into a second view, which includes a circular representationof the items of the data of the records of the database.
 18. The methodof claim 17, further comprising providing real-time updating of thefirst view in responding to a user selection in the second view.
 19. Themethod of claim 12, further comprising refining the data query of thedatabase by using the user interface.
 20. The method of claim 12,wherein the data includes a first selected geographical indicatordivided into the in-group, and wherein the data includes a seconddifferent geographical indicator divided into the out-group.